The proposed research focuses upon category learning, especially that characterized by prototype abstraction from ill-defined categories. In Part I, a spatial model for the evolution of conceptual structure and prototype abstraction is described which employs the method of multidimensional scaling. It is suggested that category learning and abstraction may be fruitfully explored via: (a) the change in dimensionality as learning progresses, and (b) the development of clusters appropriate to the learned categories in which the placement of the abstracted prototype occupies a central location. The hypothesized conceptual space is viewed as proceeding from a relatively undifferentiated state to one in which the final configuration mirrors the requisite category learning. Variables such as the number of different exemplars deining a category, the degree of original learning, the amount of time elapsing between original learning and test, etc., are suggested to be important determinants in the development of such a spatial model. With sufficient time delays, it is proposed that the configuration may show diffusion from its learned state. Measurements such as the degree of category cohesion, extent to which the abstracted prototype occupies the ideal (geometric) center of its exemplars, etc. are envisioned as providing insight into the genesis and decay of conceptual information. In Part II, a series of broadly defined subareas are described which deal with: (a) the relative contribution of common and distinctive information on abstraction at various learning phases, (b) the abstraction of category boundaries, and (c) the optimization of conceptual information. In the final section, the influence of abstraction on perceptual encoding and the retrievability of abstracted information are explored.